Abstract Data-intensive scientific workflows, typically modelled by directed acyclic graphs, consist of inter-dependent tasks that exchange significant amounts of data and are executed on parallel/distributed clusters. However, the makespan, energy or monetary costs associated with large data transfers between tasks executing on different nodes may be significant. As a result, there is scope to explore the possibility of trading some communication for computation, aiming to reduce overall communication costs. Hence, in this paper we propose a Execution Resource Provisioning for Data Intensive Scientific Workflow Execution (ERP-DISWE) for reducing the makespan, consumption of energy and cost. The proposed model has been compared with the other existing models using the Inspiral. The results show better performance when compared with the other existing models in terms of makespan, cost and energy consumption. Keywords: Makespan, Cost, Energy Consumption, Inspiral, ERP-DISWE